Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization

2026-06-22

AI Summary

Current classifier-free guidance (CFG) in diffusion models struggles to balance conditional fidelity with generation diversity and lacks effective strategies for scheduling guidance strength. This work proposes an information-theoretic adaptive CFG scheduling framework, introducing information theory into CFG optimization for the first time. By defining a target trade-off through the clean endpoint distribution and dynamically adjusting guidance strength across noise levels without explicit density estimation, the method leverages trajectory-level samples, score evaluations, and information-theoretic metrics to design an adaptive scheduling algorithm. Evaluated on state-of-the-art models such as EDM-XXL and SD-XL, the approach achieves superior distribution alignment. Experiments on ImageNet-512 and COCO demonstrate that the learned scheduling policy significantly enhances generation diversity while preserving conditional consistency, outperforming or matching fixed guidance weights.

Abstract

Diffusion models have achieved strong performance in image, text-to-image, and video generation, where conditional generation is often controlled by classifier-free guidance (CFG). CFG improves condition consistency by increasing a guidance weight, but stronger guidance typically reduces diversity and distributional coverage. It remains unclear how this consistency-coverage trade-off should be controlled across the reverse trajectory, since the distribution induced by CFG is not simply the fixed-time tilted distribution given by the guided score field. To address this issue, we propose an information-theoretic framework for CFG schedule optimization. Our approach uses a clean endpoint reference to specify the desired consistency-coverage trade-off, while optimizing the actual distribution induced by the guided sampler toward this reference. We derive trajectory-level formulas to estimate the objective from samples and score evaluations, avoiding explicit density estimation. On ImageNet-512 with EDM-XXL and COCO with SD-XL, the learned schedules achieve competitive or improved trade-offs over constant guidance and allocate guidance selectively across noise levels.

Problem

• classifier-free guidance
• consistency-coverage trade-off
• diffusion models
• schedule optimization
• conditional generation

Innovation

• classifier-free guidance
• information-theoretic optimization
• adaptive schedule
• diffusion models
• consistency-coverage trade-off